Redis Data Integration’s Data Transformation allows users to transform their data beyond the default translation of source types to Redis types. The transformation involves no coding! Instead it is described in a set of human readable YAML files, one per source table.

The ingested format and types are different from one source to another. Currently, the only supported source is Debezium. The first transformation from Debezium types to a naive JSON with Redis types is done automatically without any need for user instructions. Then, this JSON is passed on to the user defined transformation pipeline

Each job describes the transformation logic to perform on data from a single source. The source is typically a database table or collection and is specified as the full name of this table/collection. The job may include a filtering logic, to skip data that matches a condition. Other logical steps in the job will transform the data into the desired output that will be stored in Redis (as Hash or JSON).

Data transformation pipeline, high level


Each job will be in a separate YAML file. All of these files will be uploaded to Redis Data Integration using the deploy command (see Deploy configuration). If you are using the scaffold command, place the job files in the jobs folder.

Job YAML structure


  • source:

    This section describes what is the table that this job works on:

    • server_name: Logical server name (optional). Corresponds to debezium.source.topic.prefix property specified in Debezium Server’s config file
    • db: DB name (optional)
    • schema: DB schema (optional)
    • table: DB table
    • row_format: Format of the data to be transformed: data_only (default) - only payload, full - complete change record

Note: Any reference to server_name, db, schema and table properties will be treated by default as case insensitive. This can be changed by setting case_insensitive to false.

  • transform:

    This section includes a series of blocks that the data should go through. See documentation of the supported blocks and JMESPath custom functions.

  • output:

    This section includes the outputs where the data should be written to:

    • Cassandra:
      • uses: cassandra.write: Write into a Cassandra data store
      • with:
        • connection: Connection name
        • keyspace: Keyspace
        • table: Target table
        • keys: Array of key columns
        • mapping: Array of mapping columns
        • opcode_field: Name of the field in the payload that holds the operation (c - create, d - delete, u - update) for this record in the DB
    • Redis:
      • uses: redis.write: Write to a Redis data structure
      • with:
        • connection: Connection name
        • key: This allows to override the key of the record by applying a custom logic:
          • expression: Expression to execute
          • langauge: Expression language, JMESPath or SQL
    • SQL:
      • uses: relational.write: Write into a SQL-compatible data store
      • with:
        • connection: Connection name
        • schema: Schema
        • table: Target table
        • keys: Array of key columns
        • mapping: Array of mapping columns
        • opcode_field: Name of the field in the payload that holds the operation (c - create, d - delete, u - update) for this record in the DB


  • source is required.
  • Either transform and key (or both) should be specified.


This example shows how to rename a certain field (fname to first_name) in a given table (emp) using the rename_field block. It also demonstrates how to set the key of this record instead of relying on the default logic.


  server_name: redislabs
  schema: dbo
  table: emp
  - uses: rename_field
      from_field: fname
      to_field: first_name
  - uses: redis.write
      connection: target
        expression: concat(['emp:fname:',fname,':lname:',lname])
        language: jmespath

Deploy configuration

In order to deploy your jobs to the remote RDI database, run:

redis-di deploy

Deploy configuration on Kubernetes

If the RDI CLI is deployed as a pod in a Kubernetes cluster, perform these steps to deploy your jobs:

  • Create a ConfigMap from the YAML files in your jobs folder:

    kubectl create configmap redis-di-jobs --from-file=jobs/
  • Deploy your jobs:

    kubectl exec -it pod/redis-di-cli -- redis-di deploy

Note: A delay occurs between creating/modifying the ConfigMap and its availability in the redis-di-cli pod. Wait around 30 seconds before running the redis-di deploy command.

You have two options to update the ConfigMap:

  • For smaller changes, you can edit the ConfigMap directly with this command:

    kubectl edit configmap redis-di-jobs
  • For bigger changes (for example, adding another job file), edit the files in your local jobs folder and then run this command:

    kubectl create configmap redis-di-jobs --from-file=jobs/ --dry-run=client -o yaml | kubectl apply -f -

Note: You need to run kubectl exec -it pod/redis-di-cli -- redis-di deploy after updating the ConfigMap with either option.